Unsupervised image classification [1]} has shown that embedding deep learning steps into a clustering algorithm is a promising direction. The essence of image-level clustering is to see every image as a region that only contains one high-level semantic texture, but the regions’ boundaries are inaccurate. We apply this idea to the image segmentation task. Although we are going to do pixel-level segmentation, from our observations regions containing a single texture will produce similar pixel predictions. If we can find accurate boundaries to differentiate the regions containing a single texture, then the image segmentation process can be transferred into the region-level classification problem.
